The proliferation of digital cameras and scanners has lead to an explosion of digital images, creating large personal image databases. Since taking digital pictures is easy and practically free, consumers no longer restrict picture-taking to important events and special occasions. Images are being captured frequently, and of day-to-day occurrences in the consumers' life. Since a typical user has already accumulated many years of digital images, browsing the collection to find images taken during important events is already a time-consuming process for the consumer.
There has been work in grouping images into events. U.S. Pat. No. 6,606,411, assigned to A. Loui and E. Pavie, entitled “A method for automatically classifying images into events,” issued Aug. 12, 2003 and U.S. Pat. No. 6,351,556, assigned to A. Loui, and E. Pavie, entitled “A method for automatically comparing content of images for classification into events,” issued Feb. 26, 2002, disclose algorithms for clustering image content by temporal events and sub-events. According to U.S. Pat. No. 6,606,411 events have consistent color distributions, and therefore, these pictures are likely to have been taken with the same backdrop. For each sub-event, a single color and texture representation is computed for all background areas taken together. The above two patents teach how to cluster images and videos in a digital image collection into temporal events and sub-events. The terms “event” and “sub-event” are used in an objective sense to indicate the products of a computer mediated procedure that attempts to match a user's subjective perceptions of specific occurrences (corresponding to events) and divisions of those occurrences (corresponding to sub-events). Another method of automatically organzing images into events is disclosed in U.S. Pat. No. 6,915,011, assigned to A. Loui, M. Jeanson, and Z. Sun, entitled “Event clustering of images using foreground and background segmentation” issued Jul. 5, 2005. The events detected are chronologically ordered in a timeline from earliest to latest.
Using the above methods, it is possible to reduce the amount of browsing required by the user to locate a particular event by viewing representatives of the events along a timeline, instead of each image thumbnail. However, a typical user may still generate over 100 such events during a calendar year, and more prolific picture-takers can easily exceed a few hundred detected events. Many of these events depict day-to-day activities, and not important or special occasions as identified by the users. There is a need for creating a small set of important or special events (denoted as significant events in this document), that make it easy for the user to browse an overview of their collection. In addition, significant events need to be customized to a particular user's picture-taking behavior. For example, a person that rarely takes any pictures except during special occasions should have most of their images included in significant events; whereas a person that habitually takes many pictures daily may have a small fraction of all captured images included in significant events.